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| <h1>The-Trinity-Coder-7B: III Blended Coder Models - Unified Coding Intelligence</h1> | |
| <p><strong>Overview</strong></p> | |
| <p>The-Trinity-Coder-7B derives from the fusion of three distinct AI models, each specializing in unique aspects of coding and programming challenges. This model unifies the capabilities of CodeNinja, NeuralExperiment-7b-MagicCoder, and Speechless-Zephyr-Code-Functionary-7B, creating a versatile and powerful new blended model. The integration of these models was achieved through a merging technique, in order to harmonize their strengths and mitigate their individual weaknesses.</p> | |
| <h2>The Blend</h2> | |
| <ul> | |
| <li><strong>Comprehensive Coding Knowledge:</strong> TrinityAI combines over 400,000 coding instructions across a wide array of programming languages, including Python, C, C++, Rust, Java, JavaScript, and more, making it a versatile assistant for coding projects of any scale.</li> | |
| <li><strong>Advanced Code Completion:</strong> With its extensive context window, TrinityAI excels in project-level code completion, offering suggestions that are contextually relevant and syntactically accurate.</li> | |
| <li><strong>Specialized Skills Integration:</strong> By incorporating specific datasets and fine-tuning approaches, The-Trinity-Coder not only provides code completion but also excels in logical reasoning, mathematical problem-solving, and understanding complex programming concepts.</li> | |
| </ul> | |
| <h2>Model Synthesis Approach</h2> | |
| <p>The blending of the three models into TrinityAI utilized a unique merging technique that focused on preserving the core strengths of each component model:</p> | |
| <ul> | |
| <li><strong>CodeNinja:</strong> This model brings an expansive database of coding instructions, refined through Supervised Fine Tuning, making it an advanced coding assistant.</li> | |
| <li><strong>NeuralExperiment-7b-MagicCoder:</strong> Trained on datasets focusing on logical reasoning, mathematics, and programming, this model enhances TrinityAI's problem-solving and logical reasoning capabilities.</li> | |
| <li><strong>Speechless-Zephyr-Code-Functionary-7B:</strong> Part of the Moloras experiments, this model contributes enhanced coding proficiency and dynamic skill integration through its unique LoRA modules.</li> | |
| </ul> | |
| <h2>Usage and Implementation</h2> | |
| <pre><code>from transformers import AutoTokenizer, AutoModelForCausalLM | |
| model_name = "YourRepository/The-Trinity-Coder-7B" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| prompt = "Your prompt here" | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| outputs = model.generate(**inputs) | |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) | |
| </code></pre> | |
| <h2>Acknowledgments</h2> | |
| <p>Special thanks to the creators and contributors of CodeNinja, NeuralExperiment-7b-MagicCoder, and Speechless-Zephyr-Code-Functionary-7B for providing the base models for blending.</p> | |